Geologic samples are customarily examined using optical petrography to study the mineralogy of the sample. The results of this analysis can be used to estimate the content of various minerals in the sample and further to develop geological interpretations of the depositional and post-depositional processes which formed the sample. In the field of oil and gas exploration, these results are used to help predict the size and quality of underground hydrocarbon reservoirs.
The predominant method of performing optical petrography is "point counting." Notwithstanding improvements in optical lenses and automatic tally counters, optical petrography is still accomplished in essentially the same manner as it was in the 1800's. In this technique, a trained observer, or petrographer, views a magnified image of a rock sample through an optical microscope. The petrographer then classifies the point viewed under the cross-hair in the optical microscope as a specific mineral or pore space by its optical properties and records the observation for that point. The microscope stage is then advanced to additional analysis points, usually in a grid pattern, over the entire sample surface to be examined. The petrographer makes similar assessments at each point in the grid. Upon completion of the grid, the recorded values for each point evaluated are compiled in order to estimate the overall content of various minerals and the porosity in the sample.
There are a variety of problems with this traditional method of point counting. First, even when skilled petrographers are available, mineral estimates by optical petrography are subject to a significant amount of variability. The accuracy and repeatability of each optical identification is dependent upon the individual observer's visual interpretation, training, experience, and fatigue level. The traditional optical process is both tedious and laborious, and disagreement among petrographers is not uncommon.
For example, even the most commonly occurring sedimentary mineral, quartz, can be easily misidentified by an experienced petrographer as albite if it occurs as micro-crystalline quartz. This confusion occurs because it is difficult to distinguish between the optical properties of micro-crystalline quartz and albite when they occur in micrometer grain size. Crystallographic twinning, an optical property of albite, is difficult to observe in grains of this size and therefore not available to aid in distinguishing quartz from albite. This type of misidentification could be important since micro-crystalline quartz is an indicator of solution chemistry in the rock, and as such, may indicate something about the transport properties of the fluid source such as whether or not the source is locally derived. The amount of micro-crystalline quartz can also impact fluid flow properties, such as porosity and permeability, and therefore provide an indication of reservoir quality.
Another difficulty with optical point counting is that some minerals of particular interest to exploration and reservoir geologists occur on a smaller spatial scale than typical optical microscope resolution and may therefore be misidentified. For example, clay minerals have a grain size on the order of approximately 2 micrometers (.mu.m) or less while resolution of a typical petrographic optical microscope is approximately 20 .mu.m.
Furthermore, since this work is very tedious and time-consuming, such manual point counting is generally limited to a few hundred points which leads in some cases to an undesirable level of statistical uncertainty. For example, some minerals occur in low abundance (i.e. 5% or less) such that the statistical uncertainty exceeds the absolute content of that mineral in the sample. This level of error can be of great consequence for a mineral that occurs at very low levels yet has a significant effect on reservoir properties. For example, certain clays have a greater impact on fluid flow properties of a rock than others, even though they may occur in equal volume percent. It has been shown that while a certain volume percent of fiberous illite clay will decrease the permeability by over four orders of magnitude from 1000 milli-Darcics to less than 0.1 milli-Darcies, an equal volume percent of kaolinite will decrease the permeability by only two orders magnitude from 1000 milli-Darcies to 40 milli-Darcies. Therefore, misidentification of certain clays could lead to incorrect predictions about the potential productivity of an underground hydrocarbon reservoir.
Porosity estimates are also subject to variability under traditional optical point counting. Geologic samples are typically prepared by impregnating a dyed compound into the pore space of the sample. Blue epoxy is customarily used because few minerals reflect the wavelengths associated with the color blue. In this instance, the accuracy of the porosity estimation depends not only upon the subjective judgment of the petrographer with respect to the intensity of the color but also on the uniformity of the color of the dyed compound.
To reduce the errors and inconsistencies in estimates of the mineral content of geologic samples associated with optical point counting as discussed above, other methods have been proposed for mineral analysis that do not rely upon manual interpretation of visual images. Electron-beam instruments such as the scanning electron microscopes (SEM) and electron microprobes, both equipped with solid state, energy dispersive x-ray detectors (x-ray EDS) can be utilized to determine mineral abundance and porosity without the need for any visual interpretation.
Electron-beam instruments use an electron beam to excite x-ray spectra from the mineral grain by ionizing the atoms of the mineral. The ionized atoms in turn emit x-rays characteristic of their elemental chemistry. An approximation of the concentration of each of the elements in the mineral sample can be derived from the combined x-ray emission, or x-ray spectrum, so generated. The electron-beam-generated x-ray spectrum serves as a chemical fingerprint for each mineral. Such mineral x-ray spectra easily lend themselves to computer pattern recognition techniques, thus reducing the possibility of human error when compared to optical point counting. In addition, the sampling probe on a SEM has a smaller spatial resolution, approximately that of clay grain size (2 .mu.m), which is about an order of magnitude better resolution than that of the optical petrographic microscope (20 .mu.m).
A two-step process for mineral analysis based on nonnalized x-ray counts obtained using a SEM and energy-dispersive x-ray micro-analysis is disclosed in Minnis, "An Automatic Point-Counting Method for Mineralogical Assessment," The American Associalion of Petroleum Geologists Bulletin, Vol. 68, No. 6, p. 744-752 (June 1984). The system compares normalized x-ray spectra of a sample of an unknown material to a first set of 20 normalized mineral reference standards each characterized by its content of each of 12 elements. The x-ray spectrum of each point analyzed on the unknown sample is classified by determining the reference standard spectrum nearest the unknown spectrum in 12-dimensional space using an Euclidean distance function. In the second step of this process, the selection made in the first step is compared on a pass/fail basis to a second set of 18 mineral reference standards each characterized by a range of contents of each of 12 elements. Failure to fall within the pre-determined elemental ranges of one of the minerals in this second standard results in a need to further analyze the sample data point to determine whether the spectrum is unclassifiable because the sample point falls on a grain boundary between two minerals, or whether the spectrum is generated by a mineral not a member of the reference set. The disclosure is unclear, however, about how the two reference sets were obtained, and the pass/fail nature of the second step would be very sensitive to small changes in the values of the 12 elements.
Clelland, "Automated Rock Characterization with SEM/Image-Analysis Techniques," Society of Petroleum Engineers Formation Evaluation, p. 437-443 (December 1991), discloses a mineral identification system using the combination of a SEM, an energy-dispersive x-ray analyzer, and an image-processing system. As discussed in Minnis, above, the Clelland system also estimates mineral compositions based on comparison of x-ray spectra from an unknown with reference spectra. The disclosure suggests that the use of pseudo-ratios (i.e. assigning fixed values to ratios of element pairs) more effectively addresses statistical fluctuations and slight compositional variations of reference materials than the method of Minnis. This method does not differentiate some very important minerals of similar chemistry (e.g., illite vs. muscovite). Additionally, the Clelland disclosure does not specifically address what is done with points containing mineral mixtures and other unclassifiable points.
Bondarenko, "Classification of Coal Mine Dust Particles through Fuzzy Clustering of Their Energy-Dispersive Electron Microprobe X-ray Spectra," Microbeam Analysis, Vol. 3, p. 33-37 (1994), discloses a classification system using fuzzy clustering of x-ray EDS spectra to classify coal mine dust particles. The system disclosed in Bondarenko utilizes rigid threshold values but provides little insight on how to select such values. The disclosure states that the system demonstrated low reproducibility for measurements of some of the selected minerals and suggested that the set of reference minerals used was incomplete due to the high number of unclassified values. This system also provided no confidence measure in final material determinations. Porosity was not addressed at all due to the use of dust samples instead of thin section samples of rock.
Because optical point counting is labor-intensive, tedious, and is subject to human error, the above automated techniques have been proposed to decrease the time required and improve the quality of mineral identification. However, in spite of its inherent difficulties and subjectivity, manual optical petrographic point counting is still the method most widely practiced for estimation of mineral content and porosity. This practice has probably continued since optical petrographic microscopes are less expensive, more numerous, and more transportable to and in the field than many other types of equipment, and traditional university training of petrographers still involves use of these optical techniques.
A need exists for a method which permits objective and reliable identification of unknown substances. Such a method would be a particularly useful tool in the field of oil and gas exploration where estimates of both the type and abundance of various minerals, as well as the porosity, in rock samples are used to help identify valuable hydrocarbon reserves.